Matching, capturing allocation of items to unit-demand buyers, or tasks to workers, or pairs of
collaborators, is a central problem in economics. Indeed, the growing prevalence of …

The extension of classical online algorithms when provided with predictions is a new and
active research area. In this paper, we extend the primal-dual method for online algorithms …

Matching is a classic problem with a rich history and a significant impact, both on the theory
of algorithms and in practice. Recently there has been a surge of interest in the online …

Many problems in artificial intelligence require adaptively making a sequence of decisions
with uncertain outcomes under partial observability. Solving such stochastic optimization …

Algorithms in varied fields use the idea of maintaining a distribution over a certain set and
use the multiplicative update rule to iteratively change these weights. Their analyses are …

We study the problem of improving the performance of online algorithms by incorporating
machine-learned predictions. The goal is to design algorithms that are both consistent and …

A natural optimization model that formulates many online resource allocation problems is
the online linear programming (LP) problem in which the constraint matrix is revealed …

Motivated by the availability of real-time data on customer characteristics, we consider the
problem of personalizing the assortment of products for each arriving customer. Using actual …

7,552,350 B2 6/2009 Fung 2010.005 O172 A1 2/2010 Ferris 7,560,823 B2 7/2009
Schellings 2010.0057641 A1 3/2010 BOSS 7,568.360 B1 8, 2009 Bash et al. 2010.00583 …

We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-armed
bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a …